alt Hacker News"reasoning" models like GPT 5 et al do a pre-generation step where they:
- Take in the user query (input tokens)
- Break that into a game plan. Ex: "Based on user query: {query} generate a plan of action." (reasoning tokens)
- Answer (output tokens)
Because the reasoning step runs in a loop until it's run through it's action plan, it frequently uses way more tokens than the input/output step.
that was useful, thank you.
I have sooo many issues with the naming scheme of this """""AI"""" industry", it's crazy!
So the LLM gets a prompt, then creates a scheme to pull pre-weighted tokens post-user-phrasing, the constituents of which (the scheme) are called reasoning tokens, which it only explicitly distinguishes as such because there are hundreds or even thousands of output tokens to the hundreds and/or thousands of potential reasoning input tokens that were (almost) equal to the actually chosen reasoning input tokens based on the more or less adequately phrased question/prompt given ... as input ... by the user ...